Soft Compression for Lossless Image Coding Based on Shape Recognition

Letaief

2022

Entropy

Self Cite

This paper focuses on the ultimate limit theory of image compression. It proves that for an image source, there exists a coding method with shapes that can achieve the entropy rate under a certain condition where the shape-pixel ratio in the encoder/decoder is O(1/logt). Based on the new finding, an image coding framework with shapes is proposed and proved to be asymptotically optimal for stationary and ergodic processes. Moreover, the condition O(1/logt) of shape-pixel ratio in the encoder/decoder has been confirmed in the image database MNIST, which illustrates the soft compression with shape coding is a near-optimal scheme for lossless compression of images.

Section: Relations To Previous Workmentioning

confidence: 98%

Why Shape Coding? Asymptotic Analysis of the Entropy Rate for Digital Images

Letaief

2022

Entropy

Self Cite

“…Recently, a novel data-driven and data-specific image lossless compression algorithm called soft compression was proposed in [4,5]. It has superior performance on the compression ratio, outperforming popular classical standards PNG and JPEG2000, as well as recent deep learning-based algorithms [6].…”

Section: Robust Practical and Comprehensive Analysis Of Soft Compress...mentioning

confidence: 99%

Robust, practical and comprehensive analysis of soft compression image coding algorithms for big data

2022

Preprint

Self Cite

With the advancement of intelligent vision algorithms and devices, image reprocessing and secondary propagation are becoming increasingly prevalent. A large number of similar images are being produced rapidly and widely, resulting in the homogeneity and similarity of images. Moreover, it brings new challenges to compression systems, which need to exploit the potential of deep features and side information of images. However, traditional methods are incompetent for this issue. Soft compression is a novel data-driven image coding algorithm with superior performance. Compared with existing paradigms, it has distinctive characteristics: from hard to soft, from pixels to shapes, and from fixed to random. Soft compression may hold promise for human-centric/data-centric intelligent systems, making them efficient and reliable and finding potential in metaverse and digital twins, etc. In this paper, we present a comprehensive and practical analysis of soft compression, revealing the functional role of each component in the system.

“…Figure 1 shows the application of image compression in multiple scenarios. Moreover, data-driven image coding algorithms 3 are expected to empower the Metaverse and 6G, becoming the backbone of next-generation intelligent technologies.Recently, a novel data-driven and data-specific image lossless compression algorithm called soft compression was proposed in 4,5 . It has superior performance on the compression ratio, outperforming popular classical standards PNG and JPEG2000, as well as recent deep learning-based algorithms 6 .…”

mentioning

confidence: 99%

“…Recently, a novel data-driven and data-specific image lossless compression algorithm called soft compression was proposed in 4,5 . It has superior performance on the compression ratio, outperforming popular classical standards PNG and JPEG2000, as well as recent deep learning-based algorithms 6 .…”

mentioning

confidence: 99%

Robust, practical and comprehensive analysis of soft compression image coding algorithms for big data

2023

Sci Rep

Self Cite

With the advancement of intelligent vision algorithms and devices, image reprocessing and secondary propagation are becoming increasingly prevalent. A large number of similar images are being produced rapidly and widely, resulting in the homogeneity and similarity of images. Moreover, it brings new challenges to compression systems, which need to exploit the potential of deep features and side information of images. However, traditional methods are incompetent for this issue. Soft compression is a novel data-driven image coding algorithm with superior performance. Compared with existing paradigms, it has distinctive characteristics: from hard to soft, from pixels to shapes, and from fixed to random. Soft compression may hold promise for human-centric/data-centric intelligent systems, making them efficient and reliable and finding potential in the metaverse and digital twins, etc. In this paper, we present a comprehensive and practical analysis of soft compression, revealing the functional role of each component in the system.